Approaches to complete coverage path planning using neural network and grid division

Research Article
Open access

Approaches to complete coverage path planning using neural network and grid division

Yiming Zhang 1*
  • 1 Shenzhen Senior High School, Shenzhen, GuangDong(518000),China    
  • *corresponding author zhangym@cn-school.com
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230839
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

Since the goal of using complete coverage path planning is to generate a continuous and uninterrupted path that covers an area of interest while avoiding obstacles, its use could be extremely vital in today's field of robotics. Not only could it help our daily lives like lawnmowers, window cleaners, and painter robots, but it could also solve some dangerous or complex but vital problems for human beings; For example, mine detection, vacuum cleaning, and photogrammetry. This paper proposes two paths of successful approaches to Complete Coverage Path Planning: Neural Network and Grid division. After detailed data comparisons, both proved to have been efficient and successful, respectively. In addition, both plans' field applications would be placed at the end.

Keywords:

Complete Coverage Path Planning, Neural Network, Grid Division, Spanning Tree.

Zhang,Y. (2023). Approaches to complete coverage path planning using neural network and grid division. Applied and Computational Engineering,6,369-373.
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References

[1]. Galceran, E. and Carreras, M. (2013) “A survey on coverage path planning for Robotics,” Robotics and Autonomous Systems, 61(12), pp. 1258–1276. Available at: https://doi.org/10.1016/j.robot.2013.09.004.

[2]. D. Zhu, C. Tian, X. Jiang and C. Luo, "Multi-AUVs cooperative complete coverage path planning based on GBNN algorithm," 2017 29th Chinese Control And Decision Conference (CCDC), 2017, pp. 6761-6766, doi: 10.1109/CCDC.2017.7978395.

[3]. M. Arzamendia, D. G. Reina, S. T. Marin, D. Gregor and H. Tawfik, "Evolutionary Computation for Solving Path Planning of an Autonomous Surface Vehicle Using Eulerian Graphs," 2018 IEEE Congress on Evolutionary Computation (CEC), 2018, pp. 1-8, doi: 10.1109/CEC.2018.8477737.

[4]. W. Khiati, Y. Moumen, A. E. Habchi, I. Zerrouk, J. Berrich and T. Bouchentouf, "Grid Based approach (GBA): a new approach based on the grid-clustering algorithm to solve a CPP type problem for air surveillance using UAVs," 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS), 2020, pp. 1-5, doi: 10.1109/ICDS50568.2020.9268683.

[5]. G. Sanna, S. Godio and G. Guglieri, "Neural Network Based Algorithm for Multi-UAV Coverage Path Planning," 2021 International Conference on Unmanned Aircraft Systems (ICUAS), 2021, pp. 1210-1217, doi: 10.1109/ICUAS51884.2021.9476864.

[6]. H. Luo, H. Lin, T. Zhu and Z. Kang, "Complete Coverage Path Planning of UUV for Marine Mine Countermeasure Using Grid Division and Spanning Tree," 2019 Chinese Control And Decision Conference (CCDC), 2019, pp. 5016-5021, doi: 10.1109/CCDC.2019.8832742.

[7]. D. Rachmawati, Herriyance and F. Y. Putra Pakpahan, "Comparative Analysis of the Kruskal and Boruvka Algorithms in Solving Minimum Spanning Tree on Complete Graph," 2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA), 2020, pp. 55-62, doi: 10.1109/DATABIA50434.2020.9190504.

[8]. Information on https://www.javatpoint.com/kruskal-algorithm

[9]. Y. -H. Chen and C. -M. Wu, "An Improved Algorithm for Searching Maze Based on Depth-First Search," 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), 2020, pp. 1-2, doi: 10.1109/ICCE-Taiwan49838.2020.9258170.

[10]. Information on https://brilliant.org/wiki/depth-first-search-dfs/

Cite this article

Zhang,Y. (2023). Approaches to complete coverage path planning using neural network and grid division. Applied and Computational Engineering,6,369-373.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Galceran, E. and Carreras, M. (2013) “A survey on coverage path planning for Robotics,” Robotics and Autonomous Systems, 61(12), pp. 1258–1276. Available at: https://doi.org/10.1016/j.robot.2013.09.004.

[2]. D. Zhu, C. Tian, X. Jiang and C. Luo, "Multi-AUVs cooperative complete coverage path planning based on GBNN algorithm," 2017 29th Chinese Control And Decision Conference (CCDC), 2017, pp. 6761-6766, doi: 10.1109/CCDC.2017.7978395.

[3]. M. Arzamendia, D. G. Reina, S. T. Marin, D. Gregor and H. Tawfik, "Evolutionary Computation for Solving Path Planning of an Autonomous Surface Vehicle Using Eulerian Graphs," 2018 IEEE Congress on Evolutionary Computation (CEC), 2018, pp. 1-8, doi: 10.1109/CEC.2018.8477737.

[4]. W. Khiati, Y. Moumen, A. E. Habchi, I. Zerrouk, J. Berrich and T. Bouchentouf, "Grid Based approach (GBA): a new approach based on the grid-clustering algorithm to solve a CPP type problem for air surveillance using UAVs," 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS), 2020, pp. 1-5, doi: 10.1109/ICDS50568.2020.9268683.

[5]. G. Sanna, S. Godio and G. Guglieri, "Neural Network Based Algorithm for Multi-UAV Coverage Path Planning," 2021 International Conference on Unmanned Aircraft Systems (ICUAS), 2021, pp. 1210-1217, doi: 10.1109/ICUAS51884.2021.9476864.

[6]. H. Luo, H. Lin, T. Zhu and Z. Kang, "Complete Coverage Path Planning of UUV for Marine Mine Countermeasure Using Grid Division and Spanning Tree," 2019 Chinese Control And Decision Conference (CCDC), 2019, pp. 5016-5021, doi: 10.1109/CCDC.2019.8832742.

[7]. D. Rachmawati, Herriyance and F. Y. Putra Pakpahan, "Comparative Analysis of the Kruskal and Boruvka Algorithms in Solving Minimum Spanning Tree on Complete Graph," 2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA), 2020, pp. 55-62, doi: 10.1109/DATABIA50434.2020.9190504.

[8]. Information on https://www.javatpoint.com/kruskal-algorithm

[9]. Y. -H. Chen and C. -M. Wu, "An Improved Algorithm for Searching Maze Based on Depth-First Search," 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), 2020, pp. 1-2, doi: 10.1109/ICCE-Taiwan49838.2020.9258170.

[10]. Information on https://brilliant.org/wiki/depth-first-search-dfs/

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